Object recognition based on artificial intelligence (AI) is useful in applications including face or facial feature detection in mobile devices and automated teller machines (ATMs), machine recognition of facial expressions, barcodes, or gestures, and vehicle-mounted automatic warning systems. A fundamental topic of AI-based object recognition is 1-D (one-dimensional) feature classification, in which a classifier implemented within an image signal processor (ISP) examines an image to determine whether or not the image includes an object that belongs to a specific object class, such as face for face detection applications.
Training a classifier to accurately classify images involves having the classifier examine each image in a training image set as either a face or non-face, based on whether the image includes or does not include a human face. Such training requires the classifier to develop effective artificial intelligence to detect presence of a face in an image. A single round of learning involves the classifier classifying each image of the training image set. The training image set may include approximately 40,000 training images (with half of the images including a face, for example). In some applications, 3,000 rounds may be required.
To determine whether a classifier has been adequately trained, the classifier is evaluated by classifying images in a training image set. The training image need not be the same training image set used to train the classifier. Classifying images are computationally intensive and hence take a significant time to run, which increases costs of developing improved object-detection technologies.